import pandas as pd
import seaborn as sns
import plotly.express as px
import matplotlib.pyplot as plt
import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"
For this excercise, we have written the following code to load the stock dataset built into plotly express.
stocks = px.data.stocks()
stocks.head()
| date | GOOG | AAPL | AMZN | FB | NFLX | MSFT | |
|---|---|---|---|---|---|---|---|
| 0 | 2018-01-01 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 1 | 2018-01-08 | 1.018172 | 1.011943 | 1.061881 | 0.959968 | 1.053526 | 1.015988 |
| 2 | 2018-01-15 | 1.032008 | 1.019771 | 1.053240 | 0.970243 | 1.049860 | 1.020524 |
| 3 | 2018-01-22 | 1.066783 | 0.980057 | 1.140676 | 1.016858 | 1.307681 | 1.066561 |
| 4 | 2018-01-29 | 1.008773 | 0.917143 | 1.163374 | 1.018357 | 1.273537 | 1.040708 |
Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.
x = stocks.loc[:,'date']
y = stocks.loc[:,'GOOG']
fig, ax = plt.subplots()
ax.plot(x,y)
# set title
ax.set_title('y = Google stock')
# horizontal axis
ax.set_xlabel('date')
# vertical axis
ax.set_ylabel('stock value')
plt.xticks(x[::14], rotation='vertical') #show every 14th date
plt.show()
You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.
from cProfile import label
x = stocks.loc[:,'date']
y1 = stocks.loc[:,'GOOG']
y2 = stocks.loc[:,'AAPL']
y3 = stocks.loc[:,'AMZN']
y4 = stocks.loc[:, 'FB']
y5 = stocks.loc[:, 'NFLX']
y6 = stocks.loc[:, 'MSFT']
plt.plot(x, y1, label = 'GOOG')
plt.plot(x, y2, label= 'AAPL')
plt.plot(x, y3, label = 'AMZN')
plt.plot(x, y4, label= 'FB')
plt.plot(x, y5, label= 'NFLX')
plt.plot(x, y6, label= 'MSFT')
plt.ylabel('stock value')
plt.xlabel('date')
plt.title('Google stock')
plt.xticks(x[::14], rotation='vertical')
plt.legend()
plt.show()
First, load the tips dataset
tips = sns.load_dataset('tips')
tips.head()
| total_bill | tip | sex | smoker | day | time | size | |
|---|---|---|---|---|---|---|---|
| 0 | 16.99 | 1.01 | Female | No | Sun | Dinner | 2 |
| 1 | 10.34 | 1.66 | Male | No | Sun | Dinner | 3 |
| 2 | 21.01 | 3.50 | Male | No | Sun | Dinner | 3 |
| 3 | 23.68 | 3.31 | Male | No | Sun | Dinner | 2 |
| 4 | 24.59 | 3.61 | Female | No | Sun | Dinner | 4 |
Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.
Some possible questions:
On what day of the week do people (that give a tip) give the highest tip? And what is the difference between male and female?
sns.barplot(x='day', y='tip', data=tips, hue='sex')
plt.show()
Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.
Hints:
df = px.data.stocks()
fig = px.line(df, x='date', y=['GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT'], markers=True)
fig.show()
df = px.data.tips()
fig = px.scatter(df, x='total_bill', y='tip', color='sex', facet_col='smoker', facet_row='time')
fig.show()
Recreate the barplot below that shows the population of different continents for the year 2007.
Hints:
#load data
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
from unicodedata import category
df_2007 = df.query('year==2007')
fig = px.histogram(df_2007, x='pop', y='continent', orientation='h', color='continent', text_auto=True).update_yaxes(categoryorder='total ascending')
fig.update_traces(textposition='outside')
fig.show()